code: def _train_epochs(self, **params):
      remote_worker_stats = []
      for i, w in enumerate(self.remote_workers):
          stats = w.train_epochs.remote(**params)
          remote_worker_stats.append(stats)
  
      success = check_for_failure(remote_worker_stats)
      if success:
          return success, ray.get(remote_worker_stats)
      else:
          return success, None
  
imports: from bigdl.orca.learn.ray_estimator import BaseEstimator
  from bigdl.orca.ray import OrcaRayContext
  from bigdl.orca.data.file import enable_multi_fs_save, enable_multi_fs_load
  import io
  import torch
  import ray
  from ray.exceptions import RayActorError
  from abc import abstractmethod, ABCMeta
  from bigdl.orca.learn.pytorch.utils import find_free_port, check_for_failure
  from bigdl.orca.learn.utils import get_driver_node_ip
  from bigdl.dllib.utils.log4Error import invalidInputError, logging
  from typing import Dict
label_desc: Use image node_id 15 for calling the BaseRayEstimator obj's underlying member method code with example usage: obj._train_epochs() and returns: success,  ray, success,  None